CN109716443B - Motion management method and system using electromyography sensor - Google Patents

Motion management method and system using electromyography sensor Download PDF

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Publication number
CN109716443B
CN109716443B CN201780046000.7A CN201780046000A CN109716443B CN 109716443 B CN109716443 B CN 109716443B CN 201780046000 A CN201780046000 A CN 201780046000A CN 109716443 B CN109716443 B CN 109716443B
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muscle
activity
monitoring module
electromyogram
movement
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CN109716443A (en
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韩亨燮
宋坰营
权五国
金太永
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Hhs Co ltd
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Hhs Co ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q20/00—Payment architectures, schemes or protocols
    • G06Q20/30—Payment architectures, schemes or protocols characterised by the use of specific devices or networks
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/389—Electromyography [EMG]
    • A61B5/397—Analysis of electromyograms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • A63B24/0062—Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G09B19/003—Repetitive work cycles; Sequence of movements
    • G09B19/0038—Sports
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B5/00—Electrically-operated educational appliances
    • G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/10—Athletes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/25—Bioelectric electrodes therefor
    • A61B5/251—Means for maintaining electrode contact with the body
    • A61B5/256—Wearable electrodes, e.g. having straps or bands
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

In an embodiment, there is provided a motion management system using an electromyography sensor, including: a control server for receiving the movement information by interlocking with the monitoring module installed with the movement management application program through the wired/wireless communication network and providing analysis information related to the movement of the user; and a signal processing module for receiving detection signals from a plurality of electromyogram sensors attached to the body of the user, analyzing the detection signals to calculate the activity of the muscle, and providing the activity to the monitoring module. By this means, by the embodiment, the cost burden of the personal trainer can be saved, and the boring feeling when the person exercises alone can be improved. In addition, the sports can be carried out at any time and any place without being limited by sports places, so that the sports are not limited by places and time. In addition, the electromyogram sensor and the smart phone can be linked to provide visual information such as the self-movement quantity, the muscle use state and the like, so that the movement efficiency can be improved.

Description

Motion management method and system using electromyography sensor
Technical Field
The present invention relates to a method and a system for managing exercise using an electromyography sensor, and more particularly, to a method and a system for managing exercise using a wearable electromyography sensor.
Background
In recent years, along with the high development of science and technology, the living environment of people has become increasingly comfortable and convenient, but at the same time, chronic adult diseases such as hypertension, diabetes, cardiovascular diseases, and chronic fatigue due to insufficient physical activity and exercise have become more and more common.
As a result, people's interest in health has become higher and more urgently aware of the necessity of exercise, thereby starting to participate in exercise or making related plans.
But must go to a hospital or professional fitness center in order to determine the proper exercise regimen for itself, and a significant amount of time and expense must be spent in order to get guidance such as personal training.
In addition, with the development of information communication technology and the popularization of smartphones, a communication environment capable of transmitting and receiving various types of information without being physically limited in time and space has been established.
Therefore, attempts have been made to find a more logical and systematic exercise method that can reduce the cost burden of personal training and improve the feeling of boring during exercise.
In korean laid-open patent No. 10-2014-013125, a technology of providing a personalized exercise management method in a portable terminal is disclosed.
However, since the amount and efficiency of the movement of the individual cannot be measured and objective feedback information is provided, side effects such as injury and deterioration of the exercise ability may occur.
Disclosure of Invention
Technical problem
The invention aims to provide a motion management method and system using a wearable electromyography sensor.
Means for solving the problems
In an embodiment, there is provided a motion management system using an electromyography sensor, including: a control server for receiving the movement information by interlocking with the monitoring module installed with the movement management application program through the wired/wireless communication network and providing analysis information related to the movement of the user; and a signal processing module for receiving detection signals from a plurality of electromyogram sensors attached to the body of the user, analyzing the detection signals to calculate the activity of the muscle, and providing the activity to the monitoring module.
The signal processing module may include: a signal analysis unit that analyzes the detection signal to select IMFs (intrinsic mode function, intrinsic mode functions) having a threshold value or more and a maximum change rate subband; and a feature extraction unit that calculates the degree of activity of the muscle from the Intrinsic Mode Function (IMF) and the maximum rate of change subband.
The degree of activity of the muscle can be calculated as the degree of muscle tension due to muscle contraction, and the degree of fatigue of the muscle.
The muscle contraction muscle tension may be calculated by the Intrinsic Mode Function (IMF) and a root mean square value (RMS) of the maximum change rate subband, the muscle fatigue may be calculated by a median frequency, and the muscle contraction time may be calculated by a cross correlation function between the electromyogram sensors.
In addition, in an embodiment, there is also provided a motion management method using an electromyogram sensor, in a method of performing motion management by a motion management application of a plurality of electromyogram sensors and a monitoring module, including: a step of interlocking with the monitoring module through a wired/wireless communication network, receiving motion information, and receiving paste position information of the electromyogram sensor from the electromyogram sensor; a step of receiving detection signals from the electromyogram sensors after starting movement; a step of calculating the activity of the muscle by analyzing the detection signal and providing the activity to the monitoring module; and a step of obtaining an improvement scheme by analyzing the exercise information and the activity of the muscle, and feeding back the improvement scheme to the monitoring module.
The step of calculating the degree of activity of the muscle may include: a step of selecting IMF (intrinsic mode function ) and maximum change rate sub-band having a threshold value or more by analyzing the detection signal; and calculating a muscle contraction muscle tension by the Intrinsic Mode Function (IMF) and a root mean square value (RMS) of the maximum change rate subband, calculating a fatigue of the muscle by a median frequency, calculating a muscle contraction time by a cross correlation function between a plurality of electromyogram sensors, and providing the muscle contraction time as an activity of the muscle.
Effects of the invention
By the embodiment, the cost burden of the personal trainer can be saved, and the boring feeling when the person exercises alone can be improved.
In addition, the sports can be carried out at any time and any place without being limited by sports places, so that the sports are not limited by places and time. In addition, the electromyogram sensor and the smart phone can be linked to provide visual information such as the self-movement quantity, the muscle use state and the like, so that the movement efficiency can be improved.
Drawings
Fig. 1 is a block diagram of an overall system including a motion management system using an electromyography sensor to which one embodiment of the present invention is applied.
Fig. 2 is a schematic diagram illustrating the overall system of fig. 1.
Fig. 3 is a detailed configuration diagram of the electromyogram sensor.
Fig. 4 is a detailed configuration diagram of the signal processing module.
Fig. 5 is a sequence diagram illustrating the operation of the overall system in fig. 1.
Fig. 6 is a detailed sequence diagram illustrating a process of calculating the activity of the muscle of the signal processing module in fig. 5.
Detailed Description
Next, in order to facilitate the implementation of the present invention by a person having ordinary skill in the art to which the present invention pertains, embodiments to which the present invention pertains will be described in detail with reference to the accompanying drawings. However, the present invention can be realized in many different forms and is not limited to the embodiments described herein. In order to more clearly explain the present invention, parts irrelevant to the description are omitted in the drawings, and like parts are assigned like reference numerals throughout the specification.
Throughout the specification, the term "connected" between a certain portion and another portion includes not only the case of "direct connection" but also the case of "electrical connection" between the two other elements.
In the entire specification, when a certain component is described as "including" a certain component, it is not meant to exclude other components, but it is meant to include other components unless explicitly stated to the contrary. In addition, terms such as "…", "…", "… module", and the like described in the present specification denote units for processing at least one function or action, which can be realized by hardware or software or a combination of hardware and software.
Next, a preferred embodiment to which the present invention is applied will be described in detail with reference to the accompanying drawings.
Fig. 1 is a configuration diagram of an overall system including a motion management system using an electromyogram sensor to which one embodiment of the present invention is applied, fig. 2 is a schematic diagram illustrating the overall system in fig. 1, fig. 3 is a detailed configuration diagram of the electromyogram sensor, and fig. 4 is a detailed configuration diagram of a signal processing module.
As shown in fig. 1, an overall system including a motion management system 500 (hereinafter, simply referred to as "motion management server 500") using an electromyography sensor to which one embodiment of the present invention is applied includes: a monitoring module 300; a motion management server 500; an electromyography sensor 100; and a plurality of movement mechanisms (not shown).
The monitoring module 300 is a terminal that allows a user to connect to the motion management server 500 and download an installed motion management application from the motion management server 500, and includes a smart phone equipped with a display window, a notebook computer, a tablet computer, or the like.
The monitoring module 300 as described above can be interlocked with the movement management server 500 through a wired or wireless internet, and the wireless internet at this time can be wifi, bluetooth, or the like.
The monitoring module 300 has installed therein a motion management application related to the motion management server 500, and can transmit and receive various information to and from the motion management server 500 by driving the application.
The electromyography sensor 100 includes a plurality of sensor modules 110, and each sensor module 110 is implemented in the form of a Wearable Device (Wearable Device).
That is, each of the electromyogram sensor modules 110 can be directly attached to the body of the user by being manufactured in a band-shaped structure.
The electromyogram sensor module 110 as described above can detect an electromyogram related to movement while the user performs exercise and transmit a detection signal.
The electromyogram sensor module 110 may be provided with a communication unit 115 for wirelessly communicating with the signal processing module 200, and may transmit a detection signal generated in response to movement of the user to the signal processing module 200.
The plurality of sensor modules 110 of the electromyogram sensor 200 can be attached to different body parts of a user and transmit the respective detection signals at the same time.
That is, as shown in fig. 2, the present invention can be freely attached to, for example, the arm, leg, chest, and hip of the user, and the exercise effect related to the target muscle can be detected by attaching the present invention to the target muscle position when the user exercises.
Each electromyogram sensor module 110 has an inherent serial number, and the signal processing module 200 can recognize each sensor module 110 by transmitting the serial number as described above to the signal processing module 200 together with the generated detection signal.
Each electromyography sensor module 110 can have a detailed configuration as shown in fig. 3.
As shown in fig. 3, each electromyogram sensor module 110 can include: a sensor section 111; an analog/digital (a/D) converter 113; a communication section 115; and a battery 117.
The sensor unit 111 is constituted by an electromyogram sensor, and detects a biological signal related to the movement of the muscle, which is detected by electrodes attached around the muscle, thereby measuring the surface electromyogram. The electromyogram sensor can measure the amount of voltage and current flowing around the muscle and the frequency thereof by attaching two electrodes such as a reference electrode and a measurement electrode to the human body.
At this time, the potential difference formed between the two electrodes can be amplified by an amplifier of the sensor and the 60Hz power supply noise can be removed by a filter. In addition, noise of a high frequency component is removed by a low pass filter, thereby detecting an electromyogram signal.
The analog/digital (a/D) converter 113 digitizes and outputs the electromyogram signal of the sensor unit 111, and the communication unit 115 transmits the digital signal to the signal processing module via a wired or wireless network, and at this time, the communication unit 115 also transmits the serial numbers of the respective electromyogram sensors at the same time.
In addition, the electromyogram sensor modules 110 further include batteries 117, respectively, and the batteries 117 can be rechargeable batteries 117.
Further, as shown in fig. 1, the motion management server 500 may include: a signal processing module 200; and, a control server 400; the signal processing module 200 and the control server may be physically separated from each other or may be functionally separated from each other on the same PC.
The signal processing module 200 can receive various detection signals from the electromyogram sensor 100 through a wired or wireless network, and calculate the activity of the muscle, which is a valid characteristic value, by performing signal processing and interpretation thereof.
Specifically, as shown in fig. 4, the signal processing module 200 can include: a synchronization and filtering section 210; a signal analysis unit 220; and a feature extraction unit 230.
The synchronization and filtering unit 210 synchronizes the plurality of detection signals received from the respective electromyogram sensor modules 110 according to channels and performs noise filtering.
The signal analysis unit 220 includes: the 1 st analysis unit 221 and the 2 nd analysis unit 223 obtain effective feature values from the detection signals.
The 1 st analysis unit 221 may decompose the filtered detection signal into a plurality of IMFs (intrinsic mode function, intrinsic mode functions) by using an EMD (empirical mode decomposition), calculate spectral values of the respective Intrinsic Mode Functions (IMFs), and calculate Intrinsic Mode Function (IMFs) values equal to or higher than a threshold value from the harmonic characteristics and the power ratio.
The 2 nd analysis unit 223 can decompose the filtered detection signal into a plurality of frequency subbands by using DWT (discrete wavelet transform ), calculate the average, dispersion, skew, kurtosis of the respective frequency bands, and select the frequency subband having the largest change rate among the calculated change rates from the frequency subbands of different frames.
As described above, the Intrinsic Mode Function (IMFs) values and the maximum rate of change sub-bands will be defined as valid eigenvalues.
The feature extraction unit 230 calculates the degree of activity of the muscle from the selected effective feature value. Specifically, the muscle contraction muscle tone is further calculated by calculating a root mean square value (RMS) from the selected Intrinsic Mode Functions (IMFs) and the selected subbands, and the fatigue of the muscle is calculated by a median frequency (median frequency). Further, the feature extraction unit 230 analyzes the muscle contraction time using a cross-correlation function (cross-correlation) between channels.
As described above, the feature extraction unit 230 can extract and transmit the muscle contraction muscle tension, fatigue, and time as the activity of the muscle.
In addition, the control server 400 can confirm whether the user belongs to the subscriber of the exercise management service through a wired or wireless communication network, and when the user belongs to the subscriber of the exercise management service, can receive physical information of the subscriber of the exercise management service and exercise information and provide a personalized exercise plan by analyzing the same, and can also provide an improvement scheme of the current exercise scheme.
In addition, the exercise information of different participants can be accumulated and stored through archival analysis, and the problems and improvement schemes of the exercise equipment such as user preference, exercise habits of different time periods, exercise trend of different regions and exercise of not aiming at all persons of the individuals can be obtained through analysis according to time, age, gender and region.
The motion management server 500 including the signal processing module 200 and the control server 400 as described above provides a motion management application capable of displaying improvement schemes and feedback information at the time of motion as described above by being installed in the above-described monitoring module 300 and capable of transmitting start information and the like to the respective electromyogram sensor modules 110.
The exercise management system 500 as described above can perform an action in a state where a user installs an exercise management application on the monitoring module 300 such as a user's smart phone and attaches the plurality of electromyogram sensor modules 110 to a body part requiring exercise.
Next, the operation of the motion management system to which one embodiment of the present invention is applied will be described with reference to fig. 5 and 6.
Fig. 5 is a sequence diagram illustrating the operation of the overall system in fig. 1, and fig. 6 is a detailed sequence diagram illustrating the activity calculation process of the muscle of the signal processing module in fig. 5.
First, in step S100, the user selects a sports motion in a state of holding the monitoring module 300, such as a smart phone, to which the sports management application is installed, and selects a sports instrument in a case of having a sports instrument that needs to be used. And the selecting step of the sporting apparatus can be omitted without using a special apparatus.
Next, in step S110, the user drives the exercise management application in the smart phone to record the current exercise time and the physiological state of the user performing exercise. The physiological state can be, for example, sex, height, weight, age, abdominal obesity, etc., and the physiological state information can be measured using various measuring instruments such as a weight meter, a measuring tape, an InBody body composition analyzer, etc.
In addition, the body information as described above can be transmitted to the above-described movement management server 500 through a wired/wireless network.
Next, in step S120, the motion management server 500 requests the electromyogram sensor 100 for attachment position information of each sensor unit 111 of the sensor module 110, and receives the corresponding position information. At this time, the above-mentioned position information will also be transmitted to the monitoring module 300.
In step S130, the monitoring module 300 initializes the corresponding instrument and starts moving after receiving the position information.
At this time, in step S140, the monitoring module 300 can transmit corresponding exercise information such as time, instruments, physiological states, etc. to the exercise management server through the application program.
In step S150, the electromyogram sensor 100 generates a detection signal after the start of exercise and transmits the detection signal to the signal processing module 200 of the exercise management server 500.
Next, in step S160, the signal processing module 200 can calculate the activity of the muscle of the different actions from the detection signal and transmit the calculated activity to the monitoring module 300.
The process of calculating the activity of the muscle as described above is shown in fig. 6.
Specifically, first, in step S161, the detection signal is received and the detection signal is decomposed into a plurality of IMFs (intrinsic mode function, natural mode functions) by EMD (empirical mode decomposition ).
Next, in step S162, the spectral values of the respective natural mode functions (IMFs) among the corresponding natural mode functions (IMFs) are calculated, and the natural mode functions (IMFs) are selected by the harmonic characteristics and the power ratio to be equal to or higher than the critical value.
In step S164, the filtered detection signal is decomposed into a plurality of sub-bands by DWT (discrete wavelet transform ). Next, in step S165, the average, dispersion, skew, kurtosis of each frequency band are calculated, and the maximum change rate sub-band having the maximum change rate among the calculated change rates is selected from among the sub-bands of different frames.
At this time, in step S166, the Intrinsic Mode Function (IMFs) value and the maximum rate of change subband are defined as effective eigenvalues, and the activity of the muscle is calculated from the effective eigenvalues. Specifically, the muscle contraction muscle tone is further calculated by calculating a root mean square value (RMS) from the selected Intrinsic Mode Functions (IMFs) and the selected subbands, and the fatigue of the muscle is calculated by the median frequency.
Next, in step S167, the muscle contraction time is analyzed using a cross-correlation function (cross-correlation) between channels, i.e., between the respective sensor modules 110.
As described above, the muscle strength, the fatigue, and the time of muscle contraction are extracted and then transmitted to the monitoring module 300 as the activity of the muscle.
In step S170, the monitoring module 300 displays the activity of the muscle after receiving the activity. At this time, the above-mentioned exercise management application can display the activity in the form of a body map so that the user can recognize it quickly and effectively.
The control server 400 of the exercise management server 500 may determine the exercise state of the user by analyzing the activity level and exercise information of the muscle from the signal processing module 200, and may obtain an improvement scheme of the exercise state and transmit the improvement scheme to the monitoring module 300.
The monitoring module 300 can receive the application program and display the application program in the form of a movement scheme to feed back to the user, and finally, the application program is ended.
The control server can perform archival analysis on information including exercise programs and update the information to a database.
As described above, the present invention can provide accuracy and improvement of exercise by attaching a wearable electromyography sensor to a user's exercise site and reading and displaying the activity of muscles in real time during exercise, thereby improving exercise efficiency.
While the present invention has been described with reference to the above embodiments, those skilled in the art to which the present invention pertains will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present invention as set forth in the following claims.

Claims (2)

1. A motion management system using an electromyography sensor, comprising:
a control server for receiving the movement information by interlocking with the monitoring module installed with the movement management application program through the wired/wireless communication network and providing analysis information related to the movement of the user; the method comprises the steps of,
a signal processing module for receiving detection signals from a plurality of electromyogram sensors attached to the body of the user, analyzing the detection signals to calculate the activity of the muscle and providing the activity to the monitoring module,
the signal processing module includes:
a signal analysis unit including a 1 st analysis unit that decomposes the detection signal into a plurality of eigenmode functions and selects an eigenmode function equal to or higher than a threshold value, and a 2 nd analysis unit that decomposes the detection signal into a plurality of sub-bands and selects a maximum rate sub-band having a maximum rate of change; the method comprises the steps of,
a feature extraction unit for calculating the activity of the muscle from the natural mode function and the maximum rate of change subband,
as the activity of the above-mentioned muscle, the muscle contraction muscle tension, the muscle fatigue and the muscle contraction time were calculated,
the muscle contraction muscle tension is calculated from the intrinsic mode function and the root mean square value of the maximum change rate subband, the muscle fatigue is calculated from the median frequency, and the muscle contraction time is calculated from the cross correlation function between the electromyogram sensors.
2. A method for motion management using an electromyography sensor, characterized by:
in a method of performing motion management by a motion management application of a plurality of electromyography sensors and a monitoring module, comprising:
a step of interlocking with the monitoring module through a wired/wireless communication network, receiving motion information, and receiving paste position information of the electromyogram sensor from the electromyogram sensor;
a step of receiving detection signals from the electromyogram sensors after starting movement;
a step of calculating the activity of the muscle by analyzing the detection signal and providing the activity to the monitoring module; the method comprises the steps of,
a step of obtaining an improvement scheme by analyzing the exercise information and the activity of the muscle and feeding back the improvement scheme to the monitoring module,
the step of calculating the activity of the muscle includes:
decomposing the detection signal into a plurality of natural mode functions, selecting a natural mode function having a threshold value or more, and decomposing the detection signal into a plurality of sub-bands, and selecting a sub-band having a maximum rate of change;
selecting the intrinsic mode function and the sub-band with the maximum change rate; the method comprises the steps of,
and calculating a muscle contraction muscle tension by the natural mode function and the root mean square value of the maximum change rate sub-band, calculating a fatigue of the muscle by a median frequency, calculating the muscle contraction time by a cross correlation function between a plurality of electromyogram sensors, and providing the muscle contraction time as an activity of the muscle.
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